Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations14620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory176.0 B

Variable types

Numeric18
Categorical4

Alerts

Area of the house(excluding basement) is highly overall correlated with Price and 6 other fieldsHigh correlation
Built Year is highly overall correlated with number of bathrooms and 1 other fieldsHigh correlation
Price is highly overall correlated with Area of the house(excluding basement) and 4 other fieldsHigh correlation
grade of the house is highly overall correlated with Area of the house(excluding basement) and 5 other fieldsHigh correlation
living area is highly overall correlated with Area of the house(excluding basement) and 5 other fieldsHigh correlation
living_area_renov is highly overall correlated with Area of the house(excluding basement) and 4 other fieldsHigh correlation
lot area is highly overall correlated with lot_area_renovHigh correlation
lot_area_renov is highly overall correlated with lot areaHigh correlation
number of bathrooms is highly overall correlated with Area of the house(excluding basement) and 7 other fieldsHigh correlation
number of bedrooms is highly overall correlated with Area of the house(excluding basement) and 2 other fieldsHigh correlation
number of floors is highly overall correlated with Area of the house(excluding basement) and 3 other fieldsHigh correlation
number of views is highly overall correlated with waterfront presentHigh correlation
waterfront present is highly overall correlated with number of viewsHigh correlation
waterfront present is highly imbalanced (93.5%) Imbalance
number of views is highly imbalanced (72.4%) Imbalance
Area of the basement has 8842 (60.5%) zeros Zeros
Renovation Year has 13954 (95.4%) zeros Zeros

Reproduction

Analysis started2025-06-22 22:01:32.684014
Analysis finished2025-06-22 22:02:13.181363
Duration40.5 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Real number (ℝ)

Distinct241
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42604.539
Minimum42491
Maximum42734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:13.264682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42491
5-th percentile42503
Q142546
median42600
Q342662
95-th percentile42715
Maximum42734
Range243
Interquartile range (IQR)116

Descriptive statistics

Standard deviation67.347991
Coefficient of variation (CV)0.0015807703
Kurtosis-1.1308227
Mean42604.539
Median Absolute Deviation (MAD)57
Skewness0.14374721
Sum6.2287836 × 108
Variance4535.7519
MonotonicityIncreasing
2025-06-22T19:02:13.521713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42543 142
 
1.0%
42545 131
 
0.9%
42546 131
 
0.9%
42558 127
 
0.9%
42559 121
 
0.8%
42544 119
 
0.8%
42607 117
 
0.8%
42670 117
 
0.8%
42564 116
 
0.8%
42509 116
 
0.8%
Other values (231) 13383
91.5%
ValueCountFrequency (%)
42491 67
0.5%
42492 4
 
< 0.1%
42493 5
 
< 0.1%
42494 84
0.6%
42495 83
0.6%
42496 93
0.6%
42497 81
0.6%
42498 81
0.6%
42499 5
 
< 0.1%
42500 2
 
< 0.1%
ValueCountFrequency (%)
42734 45
0.3%
42733 43
0.3%
42732 64
0.4%
42730 2
 
< 0.1%
42729 38
0.3%
42727 31
0.2%
42726 62
0.4%
42725 67
0.5%
42724 2
 
< 0.1%
42723 8
 
0.1%

number of bedrooms
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3793434
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:13.614219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93871885
Coefficient of variation (CV)0.27778144
Kurtosis69.24031
Mean3.3793434
Median Absolute Deviation (MAD)1
Skewness2.6632569
Sum49406
Variance0.88119308
MonotonicityNot monotonic
2025-06-22T19:02:13.695261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 6612
45.2%
4 4724
32.3%
2 1844
 
12.6%
5 1079
 
7.4%
6 176
 
1.2%
1 136
 
0.9%
7 30
 
0.2%
8 11
 
0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 136
 
0.9%
2 1844
 
12.6%
3 6612
45.2%
4 4724
32.3%
5 1079
 
7.4%
6 176
 
1.2%
7 30
 
0.2%
8 11
 
0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 3
 
< 0.1%
8 11
 
0.1%
7 30
 
0.2%
6 176
 
1.2%
5 1079
 
7.4%
4 4724
32.3%
3 6612
45.2%

number of bathrooms
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1295828
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:13.782697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7699345
Coefficient of variation (CV)0.36154242
Kurtosis1.5881949
Mean2.1295828
Median Absolute Deviation (MAD)0.5
Skewness0.55666314
Sum31134.5
Variance0.59279913
MonotonicityNot monotonic
2025-06-22T19:02:13.896529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.5 3678
25.2%
1 2509
17.2%
1.75 2062
14.1%
2.25 1378
 
9.4%
2 1323
 
9.0%
1.5 968
 
6.6%
2.75 831
 
5.7%
3 510
 
3.5%
3.5 504
 
3.4%
3.25 424
 
2.9%
Other values (19) 433
 
3.0%
ValueCountFrequency (%)
0.5 3
 
< 0.1%
0.75 47
 
0.3%
1 2509
17.2%
1.25 7
 
< 0.1%
1.5 968
 
6.6%
1.75 2062
14.1%
2 1323
 
9.0%
2.25 1378
 
9.4%
2.5 3678
25.2%
2.75 831
 
5.7%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 1
 
< 0.1%
6.25 2
 
< 0.1%
6 3
 
< 0.1%
5.75 2
 
< 0.1%
5.5 8
0.1%
5.25 12
0.1%

living area
Real number (ℝ)

High correlation 

Distinct865
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2098.263
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:14.019015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11440
median1930
Q32570
95-th percentile3800
Maximum13540
Range13170
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation928.27572
Coefficient of variation (CV)0.44240199
Kurtosis6.0736171
Mean2098.263
Median Absolute Deviation (MAD)550
Skewness1.5383366
Sum30676605
Variance861695.81
MonotonicityNot monotonic
2025-06-22T19:02:14.152670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 93
 
0.6%
1010 92
 
0.6%
1320 91
 
0.6%
1660 90
 
0.6%
1820 88
 
0.6%
1440 86
 
0.6%
2100 85
 
0.6%
1480 84
 
0.6%
1720 84
 
0.6%
1540 84
 
0.6%
Other values (855) 13743
94.0%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
420 1
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
470 1
< 0.1%
480 2
< 0.1%
490 1
< 0.1%
500 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
7710 1
< 0.1%

lot area
Real number (ℝ)

High correlation 

Distinct7451
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15093.281
Minimum520
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:14.295609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1752.9
Q15010.75
median7620
Q310800
95-th percentile43832.3
Maximum1074218
Range1073698
Interquartile range (IQR)5789.25

Descriptive statistics

Standard deviation37919.621
Coefficient of variation (CV)2.5123511
Kurtosis164.75727
Mean15093.281
Median Absolute Deviation (MAD)2655
Skewness10.155206
Sum2.2066377 × 108
Variance1.4378977 × 109
MonotonicityNot monotonic
2025-06-22T19:02:14.429457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 269
 
1.8%
6000 176
 
1.2%
4000 172
 
1.2%
7200 149
 
1.0%
7500 82
 
0.6%
4800 80
 
0.5%
4500 75
 
0.5%
9600 73
 
0.5%
8400 72
 
0.5%
9000 72
 
0.5%
Other values (7441) 13400
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
676 1
< 0.1%
681 1
< 0.1%
696 1
< 0.1%
704 1
< 0.1%
705 1
< 0.1%
711 1
< 0.1%
713 1
< 0.1%
ValueCountFrequency (%)
1074218 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
843309 1
< 0.1%
641203 1
< 0.1%
577605 1
< 0.1%
533610 1
< 0.1%
507038 1
< 0.1%
505166 1
< 0.1%
501376 1
< 0.1%

number of floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5023598
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:14.524910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54023861
Coefficient of variation (CV)0.35959337
Kurtosis-0.52357613
Mean1.5023598
Median Absolute Deviation (MAD)0.5
Skewness0.58615758
Sum21964.5
Variance0.29185776
MonotonicityNot monotonic
2025-06-22T19:02:14.604900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 7103
48.6%
2 5666
38.8%
1.5 1311
 
9.0%
3 418
 
2.9%
2.5 118
 
0.8%
3.5 4
 
< 0.1%
ValueCountFrequency (%)
1 7103
48.6%
1.5 1311
 
9.0%
2 5666
38.8%
2.5 118
 
0.8%
3 418
 
2.9%
3.5 4
 
< 0.1%
ValueCountFrequency (%)
3.5 4
 
< 0.1%
3 418
 
2.9%
2.5 118
 
0.8%
2 5666
38.8%
1.5 1311
 
9.0%
1 7103
48.6%

waterfront present
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.3 KiB
0
14508 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14620
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

Length

2025-06-22T19:02:14.704736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:02:14.787555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14508
99.2%
1 112
 
0.8%

number of views
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.3 KiB
0
13198 
2
 
636
3
 
351
1
 
219
4
 
216

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%

Length

2025-06-22T19:02:14.877101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:02:14.950734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13198
90.3%
2 636
 
4.4%
3 351
 
2.4%
1 219
 
1.5%
4 216
 
1.5%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.3 KiB
3
9350 
4
3874 
5
1278 
2
 
100
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Length

2025-06-22T19:02:15.038648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:02:15.124823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

grade of the house
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6824213
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:15.226174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1750328
Coefficient of variation (CV)0.15295083
Kurtosis1.0480222
Mean7.6824213
Median Absolute Deviation (MAD)1
Skewness0.77758351
Sum112317
Variance1.380702
MonotonicityNot monotonic
2025-06-22T19:02:15.323228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 6011
41.1%
8 4137
28.3%
9 1828
 
12.5%
6 1324
 
9.1%
10 804
 
5.5%
11 280
 
1.9%
5 154
 
1.1%
12 55
 
0.4%
4 17
 
0.1%
13 10
 
0.1%
ValueCountFrequency (%)
4 17
 
0.1%
5 154
 
1.1%
6 1324
 
9.1%
7 6011
41.1%
8 4137
28.3%
9 1828
 
12.5%
10 804
 
5.5%
11 280
 
1.9%
12 55
 
0.4%
13 10
 
0.1%
ValueCountFrequency (%)
13 10
 
0.1%
12 55
 
0.4%
11 280
 
1.9%
10 804
 
5.5%
9 1828
 
12.5%
8 4137
28.3%
7 6011
41.1%
6 1324
 
9.1%
5 154
 
1.1%
4 17
 
0.1%

Area of the house(excluding basement)
Real number (ℝ)

High correlation 

Distinct781
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1801.7839
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:15.430964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11200
median1580
Q32240
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1040

Descriptive statistics

Standard deviation833.80996
Coefficient of variation (CV)0.46276912
Kurtosis3.4022583
Mean1801.7839
Median Absolute Deviation (MAD)460
Skewness1.4364458
Sum26342081
Variance695239.05
MonotonicityNot monotonic
2025-06-22T19:02:15.555379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010 146
 
1.0%
1200 138
 
0.9%
1300 138
 
0.9%
1140 127
 
0.9%
1400 126
 
0.9%
1220 125
 
0.9%
1340 123
 
0.8%
1060 123
 
0.8%
1320 118
 
0.8%
1100 117
 
0.8%
Other values (771) 13339
91.2%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
420 1
 
< 0.1%
430 1
 
< 0.1%
440 1
 
< 0.1%
460 1
 
< 0.1%
470 1
 
< 0.1%
480 3
< 0.1%
490 2
< 0.1%
500 2
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7680 1
< 0.1%
7320 1
< 0.1%
6640 1
< 0.1%
6430 1
< 0.1%
6420 1
< 0.1%
6380 1
< 0.1%

Area of the basement
Real number (ℝ)

Zeros 

Distinct280
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.47907
Minimum0
Maximum4820
Zeros8842
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:15.678560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3580
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)580

Descriptive statistics

Standard deviation448.55141
Coefficient of variation (CV)1.5129277
Kurtosis3.1396354
Mean296.47907
Median Absolute Deviation (MAD)0
Skewness1.6097443
Sum4334524
Variance201198.37
MonotonicityNot monotonic
2025-06-22T19:02:15.804201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8842
60.5%
800 157
 
1.1%
600 148
 
1.0%
700 147
 
1.0%
500 146
 
1.0%
400 121
 
0.8%
1000 107
 
0.7%
900 98
 
0.7%
300 96
 
0.7%
750 71
 
0.5%
Other values (270) 4687
32.1%
ValueCountFrequency (%)
0 8842
60.5%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 1
 
< 0.1%
50 10
 
0.1%
60 7
 
< 0.1%
65 1
 
< 0.1%
70 4
 
< 0.1%
80 10
 
0.1%
90 14
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

Built Year
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9264
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:16.082176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.493625
Coefficient of variation (CV)0.014964346
Kurtosis-0.67347362
Mean1970.9264
Median Absolute Deviation (MAD)23
Skewness-0.47204858
Sum28814944
Variance869.87392
MonotonicityNot monotonic
2025-06-22T19:02:16.225289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 404
 
2.8%
2005 319
 
2.2%
2006 300
 
2.1%
2004 296
 
2.0%
2003 295
 
2.0%
2007 274
 
1.9%
1977 266
 
1.8%
1978 264
 
1.8%
1968 253
 
1.7%
2008 241
 
1.6%
Other values (106) 11708
80.1%
ValueCountFrequency (%)
1900 61
0.4%
1901 21
 
0.1%
1902 20
 
0.1%
1903 33
0.2%
1904 28
 
0.2%
1905 46
0.3%
1906 67
0.5%
1907 49
0.3%
1908 54
0.4%
1909 72
0.5%
ValueCountFrequency (%)
2015 12
 
0.1%
2014 404
2.8%
2013 130
 
0.9%
2012 103
 
0.7%
2011 98
 
0.7%
2010 85
 
0.6%
2009 148
 
1.0%
2008 241
1.6%
2007 274
1.9%
2006 300
2.1%

Renovation Year
Real number (ℝ)

Zeros 

Distinct68
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.924008
Minimum0
Maximum2015
Zeros13954
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:16.365130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation416.21666
Coefficient of variation (CV)4.5776321
Kurtosis17.011306
Mean90.924008
Median Absolute Deviation (MAD)0
Skewness4.3597639
Sum1329309
Variance173236.31
MonotonicityNot monotonic
2025-06-22T19:02:16.497817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13954
95.4%
2014 76
 
0.5%
2013 30
 
0.2%
2003 27
 
0.2%
2005 23
 
0.2%
2000 22
 
0.2%
2006 22
 
0.2%
2007 20
 
0.1%
2004 19
 
0.1%
1990 18
 
0.1%
Other values (58) 409
 
2.8%
ValueCountFrequency (%)
0 13954
95.4%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 2
 
< 0.1%
1946 1
 
< 0.1%
1948 1
 
< 0.1%
1953 3
 
< 0.1%
1954 1
 
< 0.1%
1955 3
 
< 0.1%
ValueCountFrequency (%)
2015 6
 
< 0.1%
2014 76
0.5%
2013 30
 
0.2%
2012 9
 
0.1%
2011 11
 
0.1%
2010 13
 
0.1%
2009 15
 
0.1%
2008 10
 
0.1%
2007 20
 
0.1%
2006 22
 
0.2%

Postal Code
Real number (ℝ)

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122033.06
Minimum122003
Maximum122072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:16.625647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum122003
5-th percentile122006
Q1122017
median122032
Q3122048
95-th percentile122066
Maximum122072
Range69
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.082418
Coefficient of variation (CV)0.00015637089
Kurtosis-1.0583636
Mean122033.06
Median Absolute Deviation (MAD)16
Skewness0.22773543
Sum1.7841234 × 109
Variance364.13868
MonotonicityNot monotonic
2025-06-22T19:02:16.761969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122028 432
 
3.0%
122005 416
 
2.8%
122006 397
 
2.7%
122007 396
 
2.7%
122033 383
 
2.6%
122024 376
 
2.6%
122027 347
 
2.4%
122035 343
 
2.3%
122018 335
 
2.3%
122038 322
 
2.2%
Other values (60) 10873
74.4%
ValueCountFrequency (%)
122003 130
 
0.9%
122004 157
 
1.1%
122005 416
2.8%
122006 397
2.7%
122007 396
2.7%
122008 198
1.4%
122009 216
1.5%
122010 291
2.0%
122011 229
1.6%
122012 285
1.9%
ValueCountFrequency (%)
122072 132
0.9%
122071 37
 
0.3%
122070 83
 
0.6%
122069 89
 
0.6%
122068 154
1.1%
122067 179
1.2%
122066 87
 
0.6%
122065 155
1.1%
122064 253
1.7%
122063 185
1.3%

Lattitude
Real number (ℝ)

Distinct4662
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.792848
Minimum52.3859
Maximum53.0076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:16.894959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum52.3859
5-th percentile52.541
Q152.7076
median52.8064
Q352.9089
95-th percentile52.9787
Maximum53.0076
Range0.6217
Interquartile range (IQR)0.2013

Descriptive statistics

Standard deviation0.13752203
Coefficient of variation (CV)0.0026049368
Kurtosis-0.61921879
Mean52.792848
Median Absolute Deviation (MAD)0.1017
Skewness-0.52383087
Sum771831.43
Variance0.018912309
MonotonicityNot monotonic
2025-06-22T19:02:17.036873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.9255 13
 
0.1%
52.8834 13
 
0.1%
52.8947 13
 
0.1%
52.9146 13
 
0.1%
52.7745 13
 
0.1%
52.9065 12
 
0.1%
52.9271 12
 
0.1%
52.7791 12
 
0.1%
52.7702 11
 
0.1%
52.9121 11
 
0.1%
Other values (4652) 14497
99.2%
ValueCountFrequency (%)
52.3859 1
< 0.1%
52.3893 1
< 0.1%
52.3947 1
< 0.1%
52.4075 1
< 0.1%
52.4076 1
< 0.1%
52.4095 1
< 0.1%
52.4103 1
< 0.1%
52.4108 1
< 0.1%
52.4153 1
< 0.1%
52.4179 1
< 0.1%
ValueCountFrequency (%)
53.0076 1
 
< 0.1%
53.0075 1
 
< 0.1%
53.0074 1
 
< 0.1%
53.0072 2
 
< 0.1%
53.0071 1
 
< 0.1%
53.007 2
 
< 0.1%
53.0069 2
 
< 0.1%
53.0067 5
< 0.1%
53.0066 1
 
< 0.1%
53.0065 2
 
< 0.1%

Longitude
Real number (ℝ)

Distinct716
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-114.40401
Minimum-114.709
Maximum-113.505
Zeros0
Zeros (%)0.0%
Negative14620
Negative (%)100.0%
Memory size114.3 KiB
2025-06-22T19:02:17.181269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-114.709
5-th percentile-114.577
Q1-114.519
median-114.421
Q3-114.315
95-th percentile-114.168
Maximum-113.505
Range1.204
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.1413259
Coefficient of variation (CV)-0.001235323
Kurtosis0.95031537
Mean-114.40401
Median Absolute Deviation (MAD)0.101
Skewness0.87380279
Sum-1672586.6
Variance0.019973011
MonotonicityNot monotonic
2025-06-22T19:02:17.345980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-114.562 73
 
0.5%
-114.552 73
 
0.5%
-114.49 72
 
0.5%
-114.48 69
 
0.5%
-114.481 68
 
0.5%
-114.553 68
 
0.5%
-114.538 65
 
0.4%
-114.474 65
 
0.4%
-114.507 65
 
0.4%
-114.362 64
 
0.4%
Other values (706) 13938
95.3%
ValueCountFrequency (%)
-114.709 1
 
< 0.1%
-114.705 1
 
< 0.1%
-114.704 1
 
< 0.1%
-114.702 1
 
< 0.1%
-114.701 1
 
< 0.1%
-114.699 2
< 0.1%
-114.697 1
 
< 0.1%
-114.696 1
 
< 0.1%
-114.695 3
< 0.1%
-114.694 2
< 0.1%
ValueCountFrequency (%)
-113.505 1
< 0.1%
-113.506 1
< 0.1%
-113.509 1
< 0.1%
-113.542 1
< 0.1%
-113.549 1
< 0.1%
-113.554 1
< 0.1%
-113.592 1
< 0.1%
-113.595 1
< 0.1%
-113.607 1
< 0.1%
-113.663 1
< 0.1%

living_area_renov
Real number (ℝ)

High correlation 

Distinct665
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.7023
Minimum460
Maximum6110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:17.500022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile1150
Q11490
median1850
Q32380
95-th percentile3330
Maximum6110
Range5650
Interquartile range (IQR)890

Descriptive statistics

Standard deviation691.09337
Coefficient of variation (CV)0.34611739
Kurtosis1.4289442
Mean1996.7023
Median Absolute Deviation (MAD)420
Skewness1.0819589
Sum29191787
Variance477610.04
MonotonicityNot monotonic
2025-06-22T19:02:17.644358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 136
 
0.9%
1540 131
 
0.9%
1560 127
 
0.9%
1500 122
 
0.8%
1510 117
 
0.8%
1620 114
 
0.8%
1580 112
 
0.8%
1720 111
 
0.8%
1570 111
 
0.8%
1700 110
 
0.8%
Other values (655) 13429
91.9%
ValueCountFrequency (%)
460 2
< 0.1%
620 1
< 0.1%
670 1
< 0.1%
690 1
< 0.1%
700 2
< 0.1%
710 1
< 0.1%
720 1
< 0.1%
740 2
< 0.1%
750 2
< 0.1%
760 2
< 0.1%
ValueCountFrequency (%)
6110 1
 
< 0.1%
5790 4
< 0.1%
5600 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5200 1
 
< 0.1%
5170 1
 
< 0.1%
5110 1
 
< 0.1%
5080 1
 
< 0.1%
5070 1
 
< 0.1%

lot_area_renov
Real number (ℝ)

High correlation 

Distinct6835
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12753.5
Minimum651
Maximum560617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:17.775107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2021.3
Q15097.75
median7620
Q310125
95-th percentile37459.15
Maximum560617
Range559966
Interquartile range (IQR)5027.25

Descriptive statistics

Standard deviation26058.414
Coefficient of variation (CV)2.0432363
Kurtosis79.360403
Mean12753.5
Median Absolute Deviation (MAD)2520
Skewness7.7742063
Sum1.8645617 × 108
Variance6.7904096 × 108
MonotonicityNot monotonic
2025-06-22T19:02:17.950550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 301
 
2.1%
4000 256
 
1.8%
6000 179
 
1.2%
7200 138
 
0.9%
4800 102
 
0.7%
7500 101
 
0.7%
8400 77
 
0.5%
3600 76
 
0.5%
8000 73
 
0.5%
9000 72
 
0.5%
Other values (6825) 13245
90.6%
ValueCountFrequency (%)
651 1
< 0.1%
659 1
< 0.1%
660 1
< 0.1%
748 1
< 0.1%
750 2
< 0.1%
758 1
< 0.1%
794 1
< 0.1%
809 1
< 0.1%
817 1
< 0.1%
824 1
< 0.1%
ValueCountFrequency (%)
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
422967 1
< 0.1%
392040 2
< 0.1%
380279 1
< 0.1%
360000 1
< 0.1%
358934 1
< 0.1%
326097 1
< 0.1%
325393 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.3 KiB
3
4973 
2
4853 
1
4794 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14620
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Length

2025-06-22T19:02:18.226952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:02:18.286705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Most occurring characters

ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 4973
34.0%
2 4853
33.2%
1 4794
32.8%

Distance from the airport
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.950958
Minimum50
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:18.363363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q157
median65
Q373
95-th percentile79
Maximum80
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9360078
Coefficient of variation (CV)0.13758085
Kurtosis-1.2030482
Mean64.950958
Median Absolute Deviation (MAD)8
Skewness0.0061143303
Sum949583
Variance79.852236
MonotonicityNot monotonic
2025-06-22T19:02:18.472488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
54 514
 
3.5%
70 511
 
3.5%
64 500
 
3.4%
50 495
 
3.4%
79 487
 
3.3%
60 486
 
3.3%
67 485
 
3.3%
53 484
 
3.3%
56 484
 
3.3%
59 482
 
3.3%
Other values (21) 9692
66.3%
ValueCountFrequency (%)
50 495
3.4%
51 442
3.0%
52 447
3.1%
53 484
3.3%
54 514
3.5%
55 473
3.2%
56 484
3.3%
57 467
3.2%
58 474
3.2%
59 482
3.3%
ValueCountFrequency (%)
80 454
3.1%
79 487
3.3%
78 461
3.2%
77 465
3.2%
76 458
3.1%
75 466
3.2%
74 471
3.2%
73 466
3.2%
72 471
3.2%
71 465
3.2%

Price
Real number (ℝ)

High correlation 

Distinct2901
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean538932.22
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2025-06-22T19:02:18.587075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1320000
median450000
Q3645000
95-th percentile1150000
Maximum7700000
Range7622000
Interquartile range (IQR)325000

Descriptive statistics

Standard deviation367532.38
Coefficient of variation (CV)0.68196402
Kurtosis40.321918
Mean538932.22
Median Absolute Deviation (MAD)150000
Skewness4.2692977
Sum7.879189 × 109
Variance1.3508005 × 1011
MonotonicityNot monotonic
2025-06-22T19:02:18.713522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 114
 
0.8%
350000 113
 
0.8%
400000 104
 
0.7%
375000 103
 
0.7%
550000 102
 
0.7%
500000 101
 
0.7%
300000 97
 
0.7%
425000 93
 
0.6%
250000 92
 
0.6%
525000 88
 
0.6%
Other values (2891) 13613
93.1%
ValueCountFrequency (%)
78000 1
 
< 0.1%
80000 1
 
< 0.1%
82000 1
 
< 0.1%
82500 1
 
< 0.1%
83000 1
 
< 0.1%
85000 1
 
< 0.1%
86500 1
 
< 0.1%
89000 1
 
< 0.1%
90000 3
< 0.1%
92000 1
 
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4490000 1
< 0.1%
4000000 1
< 0.1%
3850000 1
< 0.1%
3800000 2
< 0.1%

Interactions

2025-06-22T19:02:10.876426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:02:10.691164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-22T19:02:18.839509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Area of the basementArea of the house(excluding basement)Built YearDateDistance from the airportLattitudeLongitudeNumber of schools nearbyPostal CodePriceRenovation Yearcondition of the housegrade of the houseliving arealiving_area_renovlot arealot_area_renovnumber of bathroomsnumber of bedroomsnumber of floorsnumber of viewswaterfront present
Area of the basement1.000-0.167-0.184-0.0120.0010.115-0.2020.014-0.0230.2540.0630.1000.0880.3280.1250.0290.0180.1920.229-0.2710.1720.152
Area of the house(excluding basement)-0.1671.0000.468-0.0140.007-0.0250.3900.009-0.0920.5540.0330.1080.7150.8430.7030.2740.2560.6880.5390.6020.0880.092
Built Year-0.1840.4681.000-0.007-0.002-0.1210.4170.014-0.0710.104-0.2240.2560.4950.3450.336-0.034-0.0170.5610.1800.5470.0470.033
Date-0.012-0.014-0.0071.0000.011-0.016-0.0140.0000.018-0.045-0.0120.011-0.034-0.023-0.030-0.009-0.015-0.027-0.016-0.0120.0000.000
Distance from the airport0.0010.007-0.0020.0111.0000.005-0.0030.0000.0120.0050.0060.0040.0050.002-0.001-0.007-0.0090.008-0.0070.0180.0000.024
Lattitude0.115-0.025-0.121-0.0160.0051.000-0.1390.011-0.3050.4430.0230.0550.1040.0320.022-0.118-0.1200.016-0.0270.0230.0660.043
Longitude-0.2020.3900.417-0.014-0.003-0.1391.0000.000-0.0550.075-0.0870.0840.2280.2890.3890.3710.3800.2630.2010.1530.0850.106
Number of schools nearby0.0140.0090.0140.0000.0000.0110.0001.0000.0000.0000.0000.0000.0150.0000.0000.0060.0060.0000.0050.0150.0200.000
Postal Code-0.023-0.092-0.0710.0180.012-0.305-0.0550.0001.000-0.2930.0170.062-0.160-0.098-0.1110.1090.120-0.115-0.047-0.1270.0400.063
Price0.2540.5540.104-0.0450.0050.4430.0750.000-0.2931.0000.1100.0240.6720.6580.5860.0800.0640.5080.3510.3310.2090.334
Renovation Year0.0630.033-0.224-0.0120.0060.023-0.0870.0000.0170.1101.0000.0670.0160.054-0.0010.0070.0080.0390.0130.0120.1090.084
condition of the house0.1000.1080.2560.0110.0040.0550.0840.0000.0620.0240.0671.0000.1310.0530.0620.0170.0000.1210.0120.1830.0260.010
grade of the house0.0880.7150.495-0.0340.0050.1040.2280.015-0.1600.6720.0160.1311.0000.7180.6680.1500.1520.6570.3830.5090.1460.133
living area0.3280.8430.345-0.0230.0020.0320.2890.000-0.0980.6580.0540.0530.7181.0000.7510.3020.2800.7420.6470.4020.1510.154
living_area_renov0.1250.7030.336-0.030-0.0010.0220.3890.000-0.1110.586-0.0010.0620.6680.7511.0000.3560.3630.5710.4490.3120.1480.088
lot area0.0290.274-0.034-0.009-0.007-0.1180.3710.0060.1090.0800.0070.0170.1500.3020.3561.0000.9060.0660.212-0.2200.0480.010
lot_area_renov0.0180.256-0.017-0.015-0.009-0.1200.3800.0060.1200.0640.0080.0000.1520.2800.3630.9061.0000.0550.196-0.2280.0420.021
number of bathrooms0.1920.6880.561-0.0270.0080.0160.2630.000-0.1150.5080.0390.1210.6570.7420.5710.0660.0551.0000.5210.5510.1100.107
number of bedrooms0.2290.5390.180-0.016-0.007-0.0270.2010.005-0.0470.3510.0130.0120.3830.6470.4490.2120.1960.5211.0000.2290.0370.000
number of floors-0.2710.6020.547-0.0120.0180.0230.1530.015-0.1270.3310.0120.1830.5090.4020.312-0.220-0.2280.5510.2291.0000.0210.008
number of views0.1720.0880.0470.0000.0000.0660.0850.0200.0400.2090.1090.0260.1460.1510.1480.0480.0420.1100.0370.0211.0000.579
waterfront present0.1520.0920.0330.0000.0240.0430.1060.0000.0630.3340.0840.0100.1330.1540.0880.0100.0210.1070.0000.0080.5791.000

Missing values

2025-06-22T19:02:12.828892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-22T19:02:13.041162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Datenumber of bedroomsnumber of bathroomsliving arealot areanumber of floorswaterfront presentnumber of viewscondition of the housegrade of the houseArea of the house(excluding basement)Area of the basementBuilt YearRenovation YearPostal CodeLattitudeLongitudeliving_area_renovlot_area_renovNumber of schools nearbyDistance from the airportPrice
04249152.50365090502.00451033702801921012200352.8645-114.557288054002582380000
14249142.50292040001.50058191010101909012200452.8878-114.470247040002511400000
24249152.75291094801.50038291001939012200452.8852-114.468294066001531200000
34249142.503310429982.00039331002001012200552.9532-114.321335042847376838000
44249132.00271045001.5004818808301929012200652.9047-114.48520604500151805000
54249132.50260047501.0004917009001951012200752.9133-114.59023804750167790000
64249153.253660119952.002310366002006012200852.7637-114.050332011241372785000
74249131.752240105782.0005815506901923012200652.9254-114.482157010578371750000
84249132.50239065501.0024814409501955012200952.8014-114.59820106550173750000
94249142.252200112501.5005713009001920012201052.9145-114.391232010814253698000
Datenumber of bedroomsnumber of bathroomsliving arealot areanumber of floorswaterfront presentnumber of viewscondition of the housegrade of the houseArea of the house(excluding basement)Area of the basementBuilt YearRenovation YearPostal CodeLattitudeLongitudeliving_area_renovlot_area_renovNumber of schools nearbyDistance from the airportPrice
146104273442.75181073501.0004712006101980012206552.6003-114.36117507350173272000
146114273431.75135076861.00037135001987012202452.5917-114.24213707686270261000
146124273431.00118053501.50046118001959012206352.7350-114.44914905350354260000
146134273431.001400104251.00047140001968012204052.5038-114.491144010425259241500
146144273431.75159079311.0003711904001979012202452.5928-114.24016807931180240000
146154273421.501556200001.00047155601957012206652.6191-114.472225017286376221700
146164273432.00168070001.50047168001968012207252.5075-114.39315407480359219200
146174273421.00107061201.00036107001962012205652.7289-114.50711306120264209000
146184273441.00103066211.00046103001955012204252.7157-114.41114206631354205000
146194273431.0090047701.0003690001969200912201852.5338-114.5529003480255146000